Bibliographic record
Abstract
An increasing volume of literature deals with different meanings of the term ‘extension’ due to the many different agricultural extension systems (AESs) in use. Acknowledging the diversity of AESs, the authors recognize that there is usually a bias towards some specific aspect of these interventions that indicates a need to consider a systemic framework for comparative studies. The main purpose of this contribution is therefore to identify such a systemic view, which could be applied to comparative studies of AESs. Three levels of analysis should be scrutinized for considering a systemic view: micro (institutional), meso (national) and macro (international). At the most basic level, all AESs are involved in both intra-actions and interactions of the extension institution. For this reason, the aim of many studies has been to evaluate the institutional functions of extension practices. The functions at this lowest level are used to predict not only how extension professionals think and act, but how they react to their different target groups. The main question at the micro level is therefore to understand how a country can reach its agri-rural development goals through extension institutions and what institutional arrangements and funding trends help to achieve those goals. At the meso level, the most important considerations are national expectations, which lead to governmental support for or restrictions on the extension institution. Socioeconomic conditions and their consequences largely determine what the extension tasks should be. The main question at this level is why a country needs extension services, which define the different missions for them in different countries. Finally, at the macro level of analysis, it is important first to consider international components and their impact on the level of socioeconomic development of particular countries and, then, the extension missions. The main issue at this level is therefore to understand what international forces and considerations affect the present situation of a country and hence create new expectations of the extension system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".